alvarovillalbaa/research

Research

Turn ambiguity into a decision artifact. Run short explicit research loops. Do not produce a search dump.

Default to one primary lane. Add overlays only when they materially improve the answer.

Output Contract (Read Before Synthesizing)

Every citation in the final artifact must be an inline markdown link [name](url). Never emit a trailing Sources: or References: block. Never emit raw URLs. If a source genuinely has no URL, fall back to plain text for that one item only.

• Good: per [r/devops](https://reddit.com/r/devops), per [TechCrunch](https://techcrunch.com/…)
• Bad (raw URL): per https://techcrunch.com/…
• Bad (plain name when URL exists): per TechCrunch
• Bad (trailing block): Sources:\n- TechCrunch\n- Reddit

Do a post-synthesis self-check before finalizing any artifact. Scan for:

1. trailing Sources: / References: / Citations: blocks — delete them
2. claims that lack a date where freshness matters — add (as of YYYY-MM) or downgrade confidence
3. inference presented as directly observed fact — label it explicitly
4. a ranking or score without visible logic — add the logic or remove the rank
5. an artifact that ends without a recommendation, constraint, or next action — add one

Query Quality Pre-Flight

Before building the source plan, check for these traps:

• Keyword trap. A query like "sales tools" or "AI" is too broad to route to a useful lane. Reframe by asking: what decision does this support? For which buyer, segment, or account?
• False freshness request. The user says "recent" but the actual decision does not require fresh evidence (e.g., "recent history of CRM pricing models" only needs the last major shift). Calibrate the freshness window to the decision, not the word "recent."
• Scope mismatch. The brief says "research this company" but the actual need is "prepare for a meeting with their head of engineering." Narrow the scope before sourcing.

If the brief has a keyword trap or scope mismatch, ask one clarifying question before starting. Do not research a badly framed question and then apologize afterward.

Operating Pattern

Use this default loop:

1. run the query quality pre-flight
2. parse the brief
3. choose the lane and artifact
4. build the minimum credible source plan
5. run a direct-evidence pass
6. run one targeted gap-closing pass
7. synthesize the answer or recommendation
8. run a reviewer pass and repair only the weakest dimension once
9. run the post-synthesis self-check from the Output Contract

Keep a compact working ledger while researching. At minimum, track:

• claim_or_fact
• source
• source_type
• published_or_observed_at
• why_it_matters
• confidence_or_gap

Do not keep browsing just because more information exists. Stop when the decision is answerable, the evidence is fresh enough, and the review pass has no blocker. If one targeted repair pass still leaves critical gaps, downgrade confidence and say exactly what is missing.

Route The Job

Pick one primary lane before sourcing:

• competitor intelligence

  • compare vendors, products, positioning, pricing, proof points, launches, or weaknesses
  • read references/competitor-intelligence.md

• diligence

  • support a buy, partner, invest, renew, or vendor-risk decision
  • read references/diligence.md

• ICP research

  • define segments, titles, triggers, exclusions, and account filters
  • read references/icp-research.md

• account research

  • prepare for outreach, meetings, prioritization, or account planning
  • read references/account-research.md

• customer research

  • answer a customer or account question with evidence and caveats
  • read references/customer-research.md

Load these overlays in addition to the primary lane when needed:

• references/research-synthesis.md

  • interviews, surveys, support tickets, usability notes, CRM notes, call transcripts, open-ended feedback, behavioral data, or study planning

• references/prospect-enrichment-and-qualification.md

  • email or domain enrichment, company-first lead research, inbound triage, follow-up ranking, or pain-point matching to a product context

• references/web-research-collection.md

  • current public-web evidence, multi-page site extraction, or reproducible collection using a crawl or scrape tool

• references/exa-advanced-search-categories.md

  • when Exa MCP tools are available and the job needs public-web company or people discovery, LinkedIn or public-bio lookup, company-list building, financial filings, research papers, independent practitioner writing, or structured lead generation at scale

• references/social-signal-pass.md

  • when current community reaction, operator sentiment, or market discourse is a material input — covering Reddit, X, Hacker News, YouTube, and GitHub as primary signal sources
  • load when the brief mentions "what people are saying", "community reaction", "practitioner sentiment", "adoption friction", or "buzz"
  • also add as a layer to competitor intelligence when promotional evidence needs third-party discourse corroboration

Do not blur lanes unless the actual decision spans them.

Intake Contract

Establish these before deep research:

1. decision_or_question
2. entity_or_scope
3. audience
4. internal_or_external
5. freshness_requirement
6. artifact

If one field is missing, infer the most likely assumption and state it. Ask only the minimum questions needed to avoid researching the wrong thing.

At minimum, the brief must contain:

• a subject, company, market, segment, customer issue, or target universe
• a practical decision, use case, or answerable question

Request Shapes

Classify the job before sourcing. Use the smallest artifact that fits.

• rapid brief

  • one focused market, company, account, or segment question

• comparison

  • competitors, vendors, segments, or options

• ranked universe

  • targets, leads, accounts, risks, or opportunities

• recommendation memo

  • go or no-go style decision support

• account brief

  • compact pre-outreach, pre-meeting, or pre-prioritization brief

• enrichment brief

  • company-first brief from an email, domain, company name, or sparse lead row

• people slate

  • shortlist of people, experts, buyers, champions, or candidate profiles

• follow-up queue

  • ranked queue with explicit hot, warm, cold, or spam buckets

• customer answer

  • customer-safe answer with caveats and evidence

• study plan

  • research plan, interview guide, survey, or method recommendation

• synthesis memo

  • themes, contradictions, segments, and opportunity areas from mixed evidence

Artifact Requirements

Every final artifact must contain:

• the direct answer, recommendation, or ranking first
• the visible logic behind that answer
• dated evidence for important time-sensitive claims
• confidence, caveats, and unresolved unknowns
• an exact next action, decision constraint, or escalation path

Keep the working ledger out of the main answer unless it helps the user audit a specific claim. Use it to make the logic visible, not to flood the output.

Execution Protocol

1. Pre-Flight And Parse The Brief

Capture:

• objective
• primary_lane
• request_shape
• entities
• geography
• timeframe
• must_include
• must_exclude
• available_materials

Also capture lane-specific details:

• ICP work: industry, size, stage, geography, tech stack, triggers, excluded segments, titles, committee roles, desired list size, and required enrichments if the output will feed prospecting
• account work: target company or person, meeting or outreach goal, known relationship context, buying roles of interest, and whether company discovery, people discovery, or both are needed
• enrichment work: input type, normalized company, channel, product context, scoring rules, disqualifiers, desired count, export format, and excluded companies or people
• customer work: customer question, workflow or issue, account context, blockers, and whether the answer can be shared externally
• synthesis work: research question, methods already used, participant or segment coverage, source materials, and deliverable expectations

2. Build A Source Plan

Choose the minimum credible source set for the decision.

Preferred source order:

1. direct sources
   • company sites, docs, pricing, product artifacts, tickets, call notes, CRM history, internal documents, first-party statements
2. high-quality secondary sources
   • trusted press, review sites, analyst notes, public datasets, industry reports
3. tertiary summaries
   • only when they point to stronger evidence

When Exa MCP tools are available and the task is public-web company or people discovery, filing retrieval, research-paper search, or independent-blog or portfolio discovery, load references/exa-advanced-search-categories.md and use its category, filter, query-variation, and structured-output rules instead of inventing a fresh search pattern mid-task.

If the evidence class is already obvious, route narrowly before widening:

1. category: "research paper" for academic papers, arXiv preprints, scientific methods, benchmarks, or literature review support
2. category: "financial report" for SEC filings, earnings reports, annual reports, investor decks, risk factors, or reporting-period evidence
3. category: "personal site" for practitioner blogs, portfolios, about pages, tutorials, and independent analysis
4. category: "company" or category: "people" for entity discovery and profile finding
5. category: "news" or uncategorized search for freshness, momentum, or cross-source context

If public-web evidence is required and a collection tool exists, use the lightest collection mode that can answer the question:

1. search when no URL exists yet
2. search plus scrape when discovery and capture should happen together
3. scrape when the URL is known
4. map plus scrape when the site is known but the exact page is not
5. crawl only when many pages from a bounded site section are required
6. interact only when clicks, forms, pagination, or rendering block static capture

If current operator or market reaction matters, load references/social-signal-pass.md and run a targeted discourse pass after the direct-source pass. The reference file defines platform routing (Reddit, X, HN, YouTube, GitHub), query decomposition into 3-5 targeted sub-queries, synthesis-by-theme rules, and the specific failure modes to avoid.

Treat discourse as signal, not ground truth.

3. Gather Evidence

Normalize every captured item into:

• entity
• claim_or_fact
• source
• source_type
• published_or_observed_at
• context
• why_it_matters

For qualitative or mixed evidence, also capture:

• observation
• quote_or_signal
• behavior_vs_stated_preference
• frequency_or_prevalence
• contradictions

For enrichment or qualification work, also capture:

• normalized_company
• company_description
• recent_activity
• momentum_signals
• key_people
• stack_or_environment_clues
• likely_pain_points
• outreach_hooks
• score_inputs

Do not interpret too early. Stabilize the facts before synthesizing.

4. Run A Gap-Closing Pass

After the first evidence pass, identify the 1-3 missing dimensions that still block the decision.

Common blockers:

• freshness is too weak
• comparison rows are not like-for-like
• the answer is still mostly promotional or anecdotal
• a customer-safe claim lacks direct support
• the ranking logic is missing one decisive input

Research only those blockers. Do not restart the whole project. If the gaps remain after this pass, mark the evidence as limited and proceed with the appropriate caveats.

5. Check Evidence Sufficiency

Use rich evidence when:

• key decision dimensions have direct support
• comparisons are like-for-like
• freshness needs are satisfied
• customer or account answers are source-backed

Use limited evidence when:

• key decision dimensions are missing
• claims are mostly promotional, indirect, or anecdotal
• evidence is stale for the question
• account or customer context is incomplete or contradictory

If evidence is limited, downgrade the artifact. Do not simulate certainty.

6. Compare, Score, Or Answer

Use explicit labels:

• fit labels: Strong, Good, Partial
• answer confidence: High, Medium, Low, Unable to determine
• lead priority: hot, warm, cold, spam

Always show what drives the ranking or confidence.

7. Synthesize

Every major finding should include:

• what appears true
• the evidence behind it
• confidence
• why it matters

When the materials are qualitative or mixed:

1. code observations before collapsing them into conclusions
2. group related codes into themes
3. note prevalence, segment differences, and outliers
4. separate behaviors from stated preferences
5. preserve contradictions until synthesis
6. triangulate across methods where possible

8. Run The Review Panel

Before finalizing, run one self-review pass using these lenses:

1. decision owner
   • does this answer the actual business decision or customer question
2. skeptic
   • which claims are inferred, stale, or still weakly supported
3. source auditor
   • are key claims dated, attributable, and like-for-like
4. external-safety reviewer
   • what cannot safely be said to a customer or prospect yet
5. operator
   • what should someone do next Monday morning

Check these dimensions:

• decision_fit
• evidence_strength
• freshness
• caveat_discipline
• actionability

Label each dimension:

• pass
  • ready to ship
• tighten
  • repair before finalizing
• block
  • unsafe or too weak to present as-is

Repair only the weakest dimension once. If a blocker remains after repair, state the blocker plainly and downgrade the recommendation or answer.

9. Present The Artifact

Only include sections the evidence supports. Omit empty sections.

Use these default output shapes:

• comparison

  • question and scope
  • matrix or evidence table
  • implications, risks, and unknowns
  • recommendation
  • next actions

• ranked universe

  • use a ranked table with Rank, Item, Why it matches or matters, Evidence, Confidence, Next step

• account brief

  • use templates/account-enrichment-brief.md

• people slate

  • use a ranked table with Rank, Person, Current role or company, Why they match, Evidence, Confidence, Next step

• follow-up queue

  • use templates/lead-follow-up-queue.md

• synthesis memo

  • use templates/research-synthesis-report.md

• study plan

  • research question and decision
  • recommended method and why
  • participant or sample plan
  • guide or survey outline
  • timeline and deliverables
  • synthesis plan

• customer answer

  • direct answer
  • confidence and caveats
  • supporting evidence
  • account-specific implications or blockers
  • recommendation, workaround, or escalation path

When the evidence is thin, use a limited-evidence note:

1. question and scope
2. what was found
3. what is still missing
4. provisional read or decision constraint
5. exact next research steps

Non-Negotiables

• Do not produce a source dump.
• Date-stamp time-sensitive claims.
• Distinguish observed facts from inference.
• Prefer primary evidence over commentary about that evidence.
• Preserve contradictions until synthesis instead of flattening them away.
• Do not rank items without visible logic.
• Do not present an external-safe answer unless the evidence supports it.
• Do not run more than one unfocused extra research pass.
• End with a recommendation, a decision constraint, or an exact next action.

Minimum Clarification Rules

If you need only one or two questions, use these:

• What decision or customer question should this research support?
• Which geography, segment, account, or timeframe matters most?
• Do you want a brief, matrix, ranked list, answer, study plan, or recommendation memo?
• If this is prospecting or inbound triage, what counts as a strong signal and what counts as disqualifying noise?

Avoid long setup interviews. If a sensible assumption still produces useful work, proceed and label it.

Failure Modes To Avoid

• answering a different business question than the one the user actually has
• collecting sources without converting them into a decision or answer
• mixing stale and fresh evidence without saying so
• forcing confidence where evidence is thin
• presenting inference as customer-safe fact
• repairing every weakness at once instead of the one blocking the artifact
• keeping template sections that the evidence does not support
• ending with "it depends" when a directional recommendation is possible

Named Antipatterns

These specific failure modes have recurred. Know them by name.

Source dump without synthesis. Running 10 searches, pasting snippets, and calling it a brief. The output contract requires a decision artifact with visible logic, not a reading list.

Confidence collapse. Every claim marked "medium" or "low" confidence because the researcher is hedging instead of distinguishing what is well-supported from what is thin. Strong claims with good primary evidence deserve High confidence.

Stale-fresh blend. Mixing a 2022 pricing page with a 2025 press release in the same evidence row without labeling the date gap. Always date-stamp time-sensitive claims.

Inference laundering. Summarizing a marketing page and presenting it as an independent finding. Primary sources describe themselves; they are not neutral evidence about themselves. Label the source type.

Discourse-as-ground-truth. Treating one upvoted Reddit thread as representative consensus. Social signal is corroboration, not primary evidence. Always cross-platform corroborate before elevating a community signal.

Missing-the-actual-question. Producing a thorough piece about the company when the user asked about a specific product feature, a specific competitor, or a specific decision. Parse the decision_or_question from the intake contract before sourcing.

Endless gap-closing. Running five research passes because "more information exists." The loop is capped: one gap-closing pass, then downgrade confidence and proceed. Research debt is acceptable; infinite loops are not.

Quality Bar

• The primary lane is explicit.
• The request shape is explicit.
• The decision or question is explicit.
• The relevant overlays are explicit.
• The source plan matches the job.
• The working ledger can explain any important claim.
• The evidence mode is explicit when data is thin or stale.
• Important claims are source-backed and date-aware.
• Ranking logic is visible when ranking is used.
• Confidence, caveats, and unknowns are explicit.
• The review pass finds no unresolved blocker.
• The output ends with a recommendation, a decision constraint, or an exact next action.